Abstract
In recent years, there has been increasing interest in automatic facial behavior analysis systems from computing communities such as vision, multimodal interaction, robotics, and affective computing. Building upon the widespread utility of prior open-source facial analysis systems, we introduce OpenFace 3.0, an open-source toolkit capable of facial landmark detection, facial action unit detection, eye-gaze estimation, and facial emotion recognition. OpenFace 3.0 contributes a lightweight unified model for facial analysis, trained with a multi-task architecture across diverse populations, head poses, lighting conditions, video resolutions, and facial analysis tasks. By leveraging the benefits of parameter sharing through a unified model and training paradigm, OpenFace 3.0 exhibits improvements in prediction performance, inference speed, and memory efficiency over similar toolkits and rivals state-of-the-art models. OpenFace 3.0 can be installed and run with a single line of code and operate in real-time without specialized hardware. OpenFace 3.0 code for training models and running the system is freely available for research purposes and supports contributions from the community.
Abstract (translated)
近年来，计算领域的视觉、多模态交互、机器人和情感计算社区对自动面部行为分析系统产生了越来越浓厚的兴趣。基于先前开源的面部分析系统的广泛应用，我们推出了OpenFace 3.0，这是一个能够进行面部标志点检测、面部动作单元检测、眼动估计以及面部情绪识别的开源工具包。 OpenFace 3.0 提供了一种轻量级的统一模型，用于面部分析，并且该模型通过多样化的人群样本、头部姿态、光照条件、视频分辨率和不同的面部分析任务进行了跨任务架构的训练。通过利用统一模型及训练范式中的参数共享带来的优势，OpenFace 3.0 在预测性能、推理速度以及内存效率方面超越了类似的工具包，并且在与现有先进模型的比较中表现出色。 OpenFace 3.0 可以用单行代码安装并运行，并能在没有专门硬件的情况下实时操作。用于训练模型和运行系统的 OpenFace 3.0 代码免费提供给研究目的使用，并支持社区贡献。
URL
https://arxiv.org/abs/2506.02891
